Adaptive robust predictive control with sample-based persistent excitation

TL;DR

This paper introduces a robust adaptive MPC method that uses sample-based tests to ensure persistent excitation, enabling reliable parameter estimation and constraint satisfaction in linear systems with uncertainties.

Contribution

It presents a novel sample-based persistent excitation approach within adaptive MPC for constrained linear systems with unknown parameters and disturbances.

Findings

Guarantees convergence of parameter estimates.

Ensures robust constraint satisfaction.

Provides input-to-state stability guarantees.

Abstract

We propose a robust adaptive Model Predictive Control (MPC) strategy with online set-based estimation for constrained linear systems with unknown parameters and bounded disturbances. A sample-based test applied to predicted trajectories is used to ensure convergence of parameter estimates by enforcing a persistence of excitation condition on the closed loop system. The control law robustly satisfies constraints and has guarantees of feasibility and input-to-state stability. Convergence of parameter set estimates to the actual system parameter vector is guaranteed under conditions on reachability and tightness of disturbance bounds.